Solar Radiation Prediction using Ant Colony Optimization and Artificial Neural Network

Md. Shahid Iqbal, Md. Monirul Kabir, Amit Shaha Surja and Abdur Rouf
Additional contact information
Md. Shahid Iqbal: Sylhet Engineering College, Bangladesh
Md. Monirul Kabir: Dhaka University of Engineering and Technology, Bangladesh
Amit Shaha Surja: Sylhet Engineering College, Bangladesh
Abdur Rouf: Sylhet Engineering College, Bangladesh

European Journal of Engineering and Technology Research, 2022, vol. 7, issue 2, 99-111

Abstract: This paper proposes a Solar Radiation Prediction Model employing Ant Colony Optimization (ACO) and Artificial Neural Network (ANN), named as SRPM. SRPM aims to incorporate the Feature Selection (FS) technique in the ANN training with the guidance of hybridizing ACO. The main reason behind using FS technique is, it can provide an improved solution from a particular problem by identifying the most salient features from the available feature set. In SRPM, the hybridizing ACO search technique utilizes the information gathered from the correlation among the features and the result of ANN training. To assist the ACO search, pheromone updating technique and measurement of heuristic information have been performed by two particular sets of rules. Thus, SRPM utilizes the benefits of using the wrapper and filter approaches in selecting the salient features during SRP task. The combination eventually creates an equipoise between exploration and exploitation of ants in the way of searching as well as strengthening the capability of global search of ACO for obtaining well qualified solution in SRPM. To evaluate the executive efficiency of SRPM, data samples were collected from BMD and NASA-SSE department. Exploratory outcomes show that SRP can select six utmost salient features by providing 99.74% and 99.78% averaged testing accuracies for BMD and NASA-SSE data samples that are composed of 12 and 15 original features, respectively. Furthermore, the proposed model successfully obtained 0.26% and 0.22% MAPE for BMD and NASA-SSE data samples, respectively with a high correlation of about 99.97% within the actual and predicted data.

Keywords: Artificial Neural Network; Ant Colony Optimization; Correlation Information; Feature Selection; Solar Radiation Prediction (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
https://eu-opensci.org/index.php/ejeng/article/view/62786 Abstract page (text/html)
https://eu-opensci.org/index.php/ejeng/article/download/62786/12788 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:epw:ejeng0:v:7:y:2022:i:2:id:62786

DOI: 10.24018/ejeng.2022.7.2.2786

Access Statistics for this article

More articles in European Journal of Engineering and Technology Research from European Open Science
Bibliographic data for series maintained by Support ().